Overview

Dataset statistics

Number of variables48
Number of observations15207
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory377.0 B

Variable types

Numeric7
Boolean1
Categorical40

Alerts

pct_disc is highly overall correlated with pct_retail_discHigh correlation
pct_retail_disc is highly overall correlated with pct_discHigh correlation
marital_status_A is highly overall correlated with hhsize_ordinalHigh correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_1 and 2 other fieldsHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with hhsize_ordinalHigh correlation
hhcomp_Single Female is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_1 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_3+ is highly overall correlated with hhsize_ordinalHigh correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
hhsize_ordinal is highly overall correlated with marital_status_A and 7 other fieldsHigh correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_18.0 is highly overall correlated with description_TypeAHigh correlation
description_TypeA is highly overall correlated with campaign_13.0 and 1 other fieldsHigh correlation
display_1 is highly imbalanced (63.7%)Imbalance
display_2 is highly imbalanced (88.6%)Imbalance
display_3 is highly imbalanced (87.1%)Imbalance
display_4 is highly imbalanced (94.8%)Imbalance
display_5 is highly imbalanced (84.1%)Imbalance
display_6 is highly imbalanced (94.9%)Imbalance
display_7 is highly imbalanced (78.4%)Imbalance
display_9 is highly imbalanced (87.6%)Imbalance
display_A is highly imbalanced (94.5%)Imbalance
mailer_C is highly imbalanced (99.8%)Imbalance
mailer_D is highly imbalanced (93.4%)Imbalance
mailer_F is highly imbalanced (99.8%)Imbalance
mailer_H is highly imbalanced (84.9%)Imbalance
mailer_J is highly imbalanced (96.9%)Imbalance
mailer_L is highly imbalanced (99.8%)Imbalance
homeowner_Probable Owner is highly imbalanced (87.5%)Imbalance
homeowner_Probable Renter is highly imbalanced (91.0%)Imbalance
homeowner_Renter is highly imbalanced (64.3%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (64.6%)Imbalance
hhcomp_Single Male is highly imbalanced (55.1%)Imbalance
kid_category_2 is highly imbalanced (51.6%)Imbalance
campaign_8.0 is highly imbalanced (98.1%)Imbalance
campaign_13.0 is highly imbalanced (98.0%)Imbalance
campaign_18.0 is highly imbalanced (97.1%)Imbalance
campaign_25.0 is highly imbalanced (99.9%)Imbalance
campaign_26.0 is highly imbalanced (99.5%)Imbalance
campaign_30.0 is highly imbalanced (99.9%)Imbalance
description_TypeA is highly imbalanced (93.9%)Imbalance
description_TypeB is highly imbalanced (99.9%)Imbalance
Unnamed: 0 has unique valuesUnique
pct_disc has 8895 (58.5%) zerosZeros
pct_retail_disc has 8962 (58.9%) zerosZeros
pct_coupon_disc has 15099 (99.3%) zerosZeros

Reproduction

Analysis started2023-05-23 14:57:15.868622
Analysis finished2023-05-23 14:57:37.158345
Duration21.29 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct15207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13935.074
Minimum0
Maximum27405
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:37.305431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1400.3
Q17024.5
median13985
Q321252.5
95-th percentile26197.7
Maximum27405
Range27405
Interquartile range (IQR)14228

Descriptive statistics

Standard deviation8103.2073
Coefficient of variation (CV)0.58149724
Kurtosis-1.2672645
Mean13935.074
Median Absolute Deviation (MAD)7205
Skewness-0.022442935
Sum2.1191068 × 108
Variance65661969
MonotonicityStrictly increasing
2023-05-23T16:57:37.458434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
18832 1
 
< 0.1%
18820 1
 
< 0.1%
18821 1
 
< 0.1%
18822 1
 
< 0.1%
18823 1
 
< 0.1%
18824 1
 
< 0.1%
18825 1
 
< 0.1%
18826 1
 
< 0.1%
18827 1
 
< 0.1%
Other values (15197) 15197
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
27405 1
< 0.1%
27404 1
< 0.1%
27403 1
< 0.1%
27402 1
< 0.1%
27401 1
< 0.1%
27400 1
< 0.1%
27399 1
< 0.1%
27398 1
< 0.1%
27397 1
< 0.1%
27396 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
True
10846 
False
4361 
ValueCountFrequency (%)
True 10846
71.3%
False 4361
28.7%
2023-05-23T16:57:37.638432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct287
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1785747
Minimum0.1
Maximum57.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:37.798356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.39
median1.79
Q32.79
95-th percentile4.79
Maximum57.57
Range57.47
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.6888433
Coefficient of variation (CV)0.77520559
Kurtosis96.385061
Mean2.1785747
Median Absolute Deviation (MAD)0.71
Skewness5.6167503
Sum33129.586
Variance2.8521918
MonotonicityNot monotonic
2023-05-23T16:57:37.978427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.49 2332
15.3%
1.99 1276
 
8.4%
0.99 1261
 
8.3%
2.89 859
 
5.6%
2.99 741
 
4.9%
1.79 675
 
4.4%
3.19 647
 
4.3%
1.5 613
 
4.0%
2.5 537
 
3.5%
1.39 513
 
3.4%
Other values (277) 5753
37.8%
ValueCountFrequency (%)
0.1 5
 
< 0.1%
0.125 1
 
< 0.1%
0.15 1
 
< 0.1%
0.2 1
 
< 0.1%
0.2 11
 
0.1%
0.25 143
0.9%
0.27 1
 
< 0.1%
0.28 2
 
< 0.1%
0.3 1
 
< 0.1%
0.32 1
 
< 0.1%
ValueCountFrequency (%)
57.57 1
 
< 0.1%
27.99 1
 
< 0.1%
21.55 1
 
< 0.1%
19.99 3
 
< 0.1%
19.49 5
< 0.1%
15.99 3
 
< 0.1%
14.99 11
0.1%
12.49 4
 
< 0.1%
12.29 4
 
< 0.1%
11.99 2
 
< 0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct433
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1303918
Minimum0
Maximum0.93079585
Zeros8895
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:38.168683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.32663317
95-th percentile0.49748744
Maximum0.93079585
Range0.93079585
Interquartile range (IQR)0.32663317

Descriptive statistics

Standard deviation0.17937756
Coefficient of variation (CV)1.3756813
Kurtosis0.47398231
Mean0.1303918
Median Absolute Deviation (MAD)0
Skewness1.1477682
Sum1982.8681
Variance0.03217631
MonotonicityNot monotonic
2023-05-23T16:57:38.353335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8895
58.5%
0.3288590604 1889
 
12.4%
0.1349480969 338
 
2.2%
0.2805755396 269
 
1.8%
0.3333333333 164
 
1.1%
0.5 158
 
1.0%
0.4949494949 148
 
1.0%
0.1039426523 143
 
0.9%
0.4974874372 120
 
0.8%
0.2163009404 107
 
0.7%
Other values (423) 2976
 
19.6%
ValueCountFrequency (%)
0 8895
58.5%
0.007222222222 1
 
< 0.1%
0.01005025126 6
 
< 0.1%
0.02044989775 1
 
< 0.1%
0.03474903475 9
 
0.1%
0.04784688995 3
 
< 0.1%
0.04938271605 1
 
< 0.1%
0.05291005291 1
 
< 0.1%
0.05527638191 19
 
0.1%
0.05917159763 3
 
< 0.1%
ValueCountFrequency (%)
0.9307958478 1
< 0.1%
0.8743718593 1
< 0.1%
0.8341708543 1
< 0.1%
0.8327759197 2
< 0.1%
0.8316498316 1
< 0.1%
0.8305084746 1
< 0.1%
0.8269896194 2
< 0.1%
0.8207885305 1
< 0.1%
0.797979798 1
< 0.1%
0.7883597884 1
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct382
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12800657
Minimum-0
Maximum0.93079585
Zeros8962
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:38.559863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median0
Q30.28434504
95-th percentile0.49748744
Maximum0.93079585
Range0.93079585
Interquartile range (IQR)0.28434504

Descriptive statistics

Standard deviation0.17757929
Coefficient of variation (CV)1.3872669
Kurtosis0.52178453
Mean0.12800657
Median Absolute Deviation (MAD)0
Skewness1.1643207
Sum1946.596
Variance0.031534403
MonotonicityNot monotonic
2023-05-23T16:57:38.738411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 8962
58.9%
0.3288590604 1853
 
12.2%
0.1349480969 339
 
2.2%
0.2805755396 274
 
1.8%
0.3333333333 164
 
1.1%
0.5 154
 
1.0%
0.4949494949 151
 
1.0%
0.1039426523 147
 
1.0%
0.4974874372 124
 
0.8%
0.2163009404 107
 
0.7%
Other values (372) 2932
 
19.3%
ValueCountFrequency (%)
-0 8962
58.9%
0.007222222222 1
 
< 0.1%
0.01005025126 6
 
< 0.1%
0.02044989775 1
 
< 0.1%
0.03474903475 9
 
0.1%
0.04784688995 3
 
< 0.1%
0.04938271605 1
 
< 0.1%
0.05291005291 1
 
< 0.1%
0.05527638191 19
 
0.1%
0.05917159763 3
 
< 0.1%
ValueCountFrequency (%)
0.9307958478 1
< 0.1%
0.8743718593 1
< 0.1%
0.8341708543 1
< 0.1%
0.8327759197 2
< 0.1%
0.8305084746 1
< 0.1%
0.8269896194 2
< 0.1%
0.797979798 1
< 0.1%
0.7883597884 1
< 0.1%
0.7759197324 1
< 0.1%
0.76 1
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0023852234
Minimum-0
Maximum0.71684588
Zeros15099
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:38.918504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median0
Q3-0
95-th percentile-0
Maximum0.71684588
Range0.71684588
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.030190347
Coefficient of variation (CV)12.657241
Kurtosis212.38719
Mean0.0023852234
Median Absolute Deviation (MAD)0
Skewness14.021251
Sum36.272092
Variance0.00091145703
MonotonicityNot monotonic
2023-05-23T16:57:39.093700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-0 15099
99.3%
0.3344481605 13
 
0.1%
0.3134796238 10
 
0.1%
0.5025125628 9
 
0.1%
0.2602230483 9
 
0.1%
0.4 7
 
< 0.1%
0.4366812227 7
 
< 0.1%
0.2 6
 
< 0.1%
0.5 4
 
< 0.1%
0.2132196162 3
 
< 0.1%
Other values (33) 40
 
0.3%
ValueCountFrequency (%)
-0 15099
99.3%
0.1002004008 1
 
< 0.1%
0.119474313 1
 
< 0.1%
0.1251564456 1
 
< 0.1%
0.1333333333 2
 
< 0.1%
0.1432664756 1
 
< 0.1%
0.1474926254 1
 
< 0.1%
0.1519756839 1
 
< 0.1%
0.159453303 1
 
< 0.1%
0.1618122977 1
 
< 0.1%
ValueCountFrequency (%)
0.7168458781 1
 
< 0.1%
0.6659707724 1
 
< 0.1%
0.5917159763 1
 
< 0.1%
0.5586592179 2
 
< 0.1%
0.5291005291 2
 
< 0.1%
0.5050505051 1
 
< 0.1%
0.5025125628 9
0.1%
0.5 4
< 0.1%
0.4566210046 1
 
< 0.1%
0.4366812227 7
< 0.1%

display_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14154 
1
 
1053

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Length

2023-05-23T16:57:39.262181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:39.408626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14975 
1
 
232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Length

2023-05-23T16:57:39.518360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:39.659629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14936 
1
 
271

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Length

2023-05-23T16:57:39.808711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:40.058383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15117 
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Length

2023-05-23T16:57:40.223580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:40.368643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14855 
1
 
352

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Length

2023-05-23T16:57:40.508548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:40.648374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15120 
1
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Length

2023-05-23T16:57:40.788354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:40.933354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14685 
1
 
522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Length

2023-05-23T16:57:41.058493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:41.222222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14948 
1
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Length

2023-05-23T16:57:41.358297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:41.513617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

display_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15112 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Length

2023-05-23T16:57:41.618484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:41.768490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13509 
1
1698 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Length

2023-05-23T16:57:41.893562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:42.028616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T16:57:42.158447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:42.318537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

mailer_D
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15089 
1
 
118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Length

2023-05-23T16:57:42.428648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:42.568316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

mailer_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T16:57:42.698361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:42.850239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14878 
1
 
329

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Length

2023-05-23T16:57:42.983359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:43.118592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

mailer_J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15158 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Length

2023-05-23T16:57:43.248433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:43.406677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

mailer_L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T16:57:43.528676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:43.668383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
8551 
1
6656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Length

2023-05-23T16:57:43.758408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:43.868393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring characters

ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13106 
1
2101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Length

2023-05-23T16:57:43.953402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:44.053708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
1
9675 
0
5532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Length

2023-05-23T16:57:44.138392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:44.249453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring characters

ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14946 
1
 
261

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Length

2023-05-23T16:57:44.338538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:44.438839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15033 
1
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Length

2023-05-23T16:57:44.518451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:44.618478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14177 
1
 
1030

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Length

2023-05-23T16:57:44.698468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:44.788308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14191 
1
 
1016

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Length

2023-05-23T16:57:44.878490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:44.968711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10766 
1
4441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Length

2023-05-23T16:57:45.053722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:45.138840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring characters

ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10825 
1
4382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Length

2023-05-23T16:57:45.698441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:45.793514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring characters

ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12825 
1
2382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Length

2023-05-23T16:57:45.880808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:45.968439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13781 
1
1426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Length

2023-05-23T16:57:46.056315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:46.148841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12976 
1
2231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Length

2023-05-23T16:57:46.230941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:46.318502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13612 
1
1595 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Length

2023-05-23T16:57:46.398390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:46.498809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

kid_category_3+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13474 
1
1733 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Length

2023-05-23T16:57:46.578818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:46.668715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
1
9648 
0
5559 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Length

2023-05-23T16:57:46.748443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:46.838437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring characters

ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

age_ordinal
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4699809
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:46.918402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1656795
Coefficient of variation (CV)0.33593255
Kurtosis0.046872978
Mean3.4699809
Median Absolute Deviation (MAD)1
Skewness0.074164978
Sum52768
Variance1.3588088
MonotonicityNot monotonic
2023-05-23T16:57:46.998329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6191
40.7%
3 4103
27.0%
2 2329
 
15.3%
6 1033
 
6.8%
5 822
 
5.4%
1 729
 
4.8%
ValueCountFrequency (%)
1 729
 
4.8%
2 2329
 
15.3%
3 4103
27.0%
4 6191
40.7%
5 822
 
5.4%
6 1033
 
6.8%
ValueCountFrequency (%)
6 1033
 
6.8%
5 822
 
5.4%
4 6191
40.7%
3 4103
27.0%
2 2329
 
15.3%
1 729
 
4.8%

income_ordinal
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.287729
Minimum10
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T16:57:47.078433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q129.5
median62
Q387
95-th percentile162
Maximum250
Range240
Interquartile range (IQR)57.5

Descriptive statistics

Standard deviation51.554096
Coefficient of variation (CV)0.75495402
Kurtosis2.4437499
Mean68.287729
Median Absolute Deviation (MAD)25
Skewness1.5453192
Sum1038451.5
Variance2657.8248
MonotonicityNot monotonic
2023-05-23T16:57:47.163393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
62 3737
24.6%
39.5 2907
19.1%
87 1875
12.3%
29.5 1520
10.0%
19.5 1176
 
7.7%
10 1146
 
7.5%
137 913
 
6.0%
112 625
 
4.1%
162 621
 
4.1%
250 369
 
2.4%
Other values (2) 318
 
2.1%
ValueCountFrequency (%)
10 1146
 
7.5%
19.5 1176
 
7.7%
29.5 1520
10.0%
39.5 2907
19.1%
62 3737
24.6%
87 1875
12.3%
112 625
 
4.1%
137 913
 
6.0%
162 621
 
4.1%
187 260
 
1.7%
ValueCountFrequency (%)
250 369
 
2.4%
224.5 58
 
0.4%
187 260
 
1.7%
162 621
 
4.1%
137 913
 
6.0%
112 625
 
4.1%
87 1875
12.3%
62 3737
24.6%
39.5 2907
19.1%
29.5 1520
10.0%

hhsize_ordinal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
2.0
5282 
1.0
4603 
3.0
2361 
5.0
1588 
4.0
1373 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45621
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 5282
34.7%
1.0 4603
30.3%
3.0 2361
15.5%
5.0 1588
 
10.4%
4.0 1373
 
9.0%

Length

2023-05-23T16:57:47.248585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:47.358806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 5282
34.7%
1.0 4603
30.3%
3.0 2361
15.5%
5.0 1588
 
10.4%
4.0 1373
 
9.0%

Most occurring characters

ValueCountFrequency (%)
. 15207
33.3%
0 15207
33.3%
2 5282
 
11.6%
1 4603
 
10.1%
3 2361
 
5.2%
5 1588
 
3.5%
4 1373
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30414
66.7%
Other Punctuation 15207
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15207
50.0%
2 5282
 
17.4%
1 4603
 
15.1%
3 2361
 
7.8%
5 1588
 
5.2%
4 1373
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 15207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45621
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15207
33.3%
0 15207
33.3%
2 5282
 
11.6%
1 4603
 
10.1%
3 2361
 
5.2%
5 1588
 
3.5%
4 1373
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15207
33.3%
0 15207
33.3%
2 5282
 
11.6%
1 4603
 
10.1%
3 2361
 
5.2%
5 1588
 
3.5%
4 1373
 
3.0%

campaign_8.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15180 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Length

2023-05-23T16:57:47.458368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:47.549397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15178 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Length

2023-05-23T16:57:47.628816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:47.718683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

campaign_18.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15162 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Length

2023-05-23T16:57:47.798316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:47.888434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

campaign_25.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T16:57:47.968455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:48.068675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

campaign_26.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15201 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Length

2023-05-23T16:57:48.138355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:48.248715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T16:57:48.323524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:48.418489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

description_TypeA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15099 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Length

2023-05-23T16:57:48.508410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:48.598369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T16:57:48.678803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T16:57:48.778445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Interactions

2023-05-23T16:57:34.818678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:29.278536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.158525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.028704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.868668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.722429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.612436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:34.930551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:29.408441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.275706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.148324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.993738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.849442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.728805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:35.048331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:29.538883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.402925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.268784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.118793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.968346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.858516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:35.168646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:29.658474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.529841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.388447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.228507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.098655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.978470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:35.280010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:29.784841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.658652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.503856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.339871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.218537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:34.098517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:35.408624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:29.911007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.788475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.625905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.478844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.353415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:34.223583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:35.534735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.038718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:30.909541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:31.743368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:32.603891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:33.485219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T16:57:34.713589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-23T16:57:48.913884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discage_ordinalincome_ordinalfirst_purchasedisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownhhsize_ordinalcampaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
Unnamed: 01.000-0.0170.0490.0490.004-0.0140.0410.0490.0380.0310.0230.0280.0340.0190.0260.0120.0440.0360.0280.0000.0000.0160.0270.0000.1140.1480.1670.2030.1400.1750.1440.0760.1060.0810.2110.1310.1470.1750.1000.1590.0170.0000.0240.0060.0000.0010.0200.006
shelf_price-0.0171.000-0.001-0.0080.064-0.0010.1160.0550.0000.0090.0050.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0260.0000.0280.0080.0000.0000.0000.0100.0260.0200.0140.0000.0000.0180.0050.0070.0000.1850.0000.0000.0000.0000.0940.000
pct_disc0.049-0.0011.0000.9880.124-0.012-0.0710.1500.2180.1070.0870.0680.0780.0300.0910.0670.0110.4090.0000.1430.0000.1830.0790.0450.0480.0280.0700.0230.0000.0550.0380.0280.0540.0160.0460.0000.0260.0110.0410.0390.0630.0500.0380.0170.0380.0000.0320.017
pct_retail_disc0.049-0.0080.9881.000-0.015-0.010-0.0750.1520.2210.1080.0890.0770.0800.0310.0910.0680.0110.4130.0000.1430.0000.1870.0800.0460.0520.0280.0750.0210.0000.0560.0420.0310.0530.0170.0510.0120.0300.0130.0420.0390.0640.0190.0320.0170.0460.0000.0360.017
pct_coupon_disc0.0040.0640.124-0.0151.000-0.0100.0270.0260.0000.0000.0000.0240.0000.0000.0210.0000.0000.0000.0000.0000.0000.0330.0000.0000.0140.0300.0240.0000.0000.0140.0000.0150.0000.0000.0240.0110.0000.0200.0000.0090.0000.2050.2150.0000.0700.0000.1860.000
age_ordinal-0.014-0.001-0.012-0.010-0.0101.0000.0410.0700.0000.0220.0100.0060.0340.0020.0040.0130.0240.0350.0000.0200.0000.0450.0050.0000.1940.2030.2620.0780.0630.2370.2200.2150.1520.0970.1220.1650.1200.1710.2240.1470.0150.0290.0500.0000.0240.0000.0330.000
income_ordinal0.0410.116-0.071-0.0750.0270.0411.0000.0710.0740.0270.0180.0200.0170.0180.0150.0000.0400.0530.0000.0200.0000.0150.0320.0000.2730.1820.3290.0920.1460.1860.2100.2210.1370.2240.1460.1330.1010.2100.1790.1630.0000.0130.0000.0210.0000.0000.0160.021
first_purchase0.0490.0550.1500.1520.0260.0700.0711.0000.0100.0120.0130.0120.0170.0000.0000.0120.0090.0320.0000.0000.0000.0000.0180.0000.0220.0120.0040.0180.0150.0210.0070.0320.0060.0340.0160.0320.0000.0080.0360.0520.0140.0150.0330.0000.0040.0000.0380.000
display_10.0380.0000.2180.2210.0000.0000.0740.0101.0000.0320.0350.0180.0400.0170.0500.0340.0180.1520.0000.0710.0000.0810.0220.0000.0000.0000.0090.0080.0020.0080.0190.0000.0080.0000.0010.0000.0100.0000.0080.0000.0000.0040.0090.0000.0000.0000.0090.000
display_20.0310.0090.1070.1080.0000.0220.0270.0120.0321.0000.0120.0000.0150.0000.0200.0120.0000.0700.0000.0140.0000.0380.0000.0000.0150.0140.0040.0000.0000.0000.0290.0000.0110.0000.0000.0080.0200.0150.0070.0060.0110.0000.0000.0000.0000.0000.0000.000
display_30.0230.0050.0870.0890.0000.0100.0180.0130.0350.0121.0000.0000.0170.0000.0230.0140.0000.0570.0000.0000.0000.0000.0000.0000.0130.0000.0010.0000.0000.0130.0000.0090.0000.0070.0110.0140.0000.0000.0090.0140.0000.0000.0000.0000.0060.0000.0000.000
display_40.0280.0000.0680.0770.0240.0060.0200.0120.0180.0000.0001.0000.0040.0000.0090.0000.0000.0270.0000.0000.0000.0000.0000.0000.0190.0000.0130.0000.0000.0080.0130.0040.0000.0000.0030.0000.0000.0060.0130.0080.0000.0000.0000.0000.0000.0000.0000.000
display_50.0340.0000.0780.0800.0000.0340.0170.0170.0400.0150.0170.0041.0000.0030.0270.0170.0050.0330.0000.0000.0000.0000.0000.0000.0200.0030.0370.0160.0060.0130.0380.0000.0130.0000.0020.0080.0180.0120.0110.0190.0180.0000.0000.0000.0000.0240.0000.000
display_60.0190.0000.0300.0310.0000.0020.0180.0000.0170.0000.0000.0000.0031.0000.0090.0000.0000.0260.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0020.0080.0000.0000.0000.0000.0160.0000.0000.0050.0130.0270.0000.0000.0000.0000.0000.0040.000
display_70.0260.0000.0910.0910.0210.0040.0150.0000.0500.0200.0230.0090.0270.0091.0000.0220.0100.0760.0000.0000.0000.0000.0000.0000.0000.0000.0130.0130.0240.0370.0000.0000.0200.0000.0000.0000.0000.0000.0000.0120.0820.0000.0000.0000.0000.0000.0280.000
display_90.0120.0000.0670.0680.0000.0130.0000.0120.0340.0120.0140.0000.0170.0000.0221.0000.0000.0450.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0070.0000.0000.0130.0070.0100.0000.0000.0000.0000.0000.0000.000
display_A0.0440.0000.0110.0110.0000.0240.0400.0090.0180.0000.0000.0000.0050.0000.0100.0001.0000.0000.0000.0000.0000.0020.0160.0000.0000.0000.0170.0000.0000.0000.0000.0140.0220.0000.0320.0000.0360.0000.0080.0390.0000.0000.0000.0000.0000.0000.0000.000
mailer_A0.0360.0040.4090.4130.0000.0350.0530.0320.1520.0700.0570.0270.0330.0260.0760.0450.0001.0000.0000.0290.0000.0510.0160.0000.0070.0000.0330.0030.0000.0130.0100.0150.0270.0000.0170.0140.0000.0140.0210.0290.0560.0240.0150.0000.0170.0000.0350.000
mailer_C0.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0230.0000.0040.0330.0000.0000.0000.0000.0000.0000.0000.000
mailer_D0.0000.0000.1430.1430.0000.0200.0200.0000.0710.0140.0000.0000.0000.0000.0000.0000.0000.0290.0001.0000.0000.0070.0000.0000.0160.0130.0060.0000.0000.0070.0310.0000.0000.0000.0000.0220.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_F0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0210.0040.0290.0000.0000.0000.0000.0000.0000.0000.000
mailer_H0.0160.0000.1830.1870.0330.0450.0150.0000.0810.0380.0000.0000.0000.0000.0000.0000.0020.0510.0000.0070.0001.0000.0000.0000.0000.0040.0000.0000.0000.0120.0000.0000.0000.0080.0100.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.000
mailer_J0.0270.0000.0790.0800.0000.0050.0320.0180.0220.0000.0000.0000.0000.0800.0000.0000.0160.0160.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0060.0130.0000.0110.0060.0000.0000.0100.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.000
mailer_L0.0000.0000.0450.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
marital_status_A0.1140.0260.0480.0520.0140.1940.2730.0220.0000.0150.0130.0190.0200.0000.0000.0230.0000.0070.0000.0160.0000.0000.0000.0001.0000.3530.4630.0100.0900.0430.1220.4290.1120.2310.1920.1650.0680.2740.3460.6110.0080.0000.0000.0000.0000.0000.0020.000
marital_status_B0.1480.0000.0280.0280.0300.2030.1820.0120.0000.0140.0000.0000.0030.0000.0000.0000.0000.0000.0000.0130.0000.0040.0000.0000.3531.0000.1460.0150.0000.2620.2000.2010.1230.2130.1600.0890.0060.0280.0910.2620.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Homeowner0.1670.0280.0700.0750.0240.2620.3290.0040.0090.0040.0010.0130.0370.0000.0130.0000.0170.0330.0000.0060.0000.0000.0000.0000.4630.1461.0000.1740.1410.3560.1170.2560.2450.2850.1790.1000.0310.1240.1760.4330.0110.0000.0000.0000.0000.0000.0080.000
homeowner_Probable Owner0.2030.0080.0230.0210.0000.0780.0920.0180.0080.0000.0000.0000.0160.0000.0130.0000.0000.0030.0000.0000.0000.0000.0000.0000.0100.0150.1741.0000.0090.0340.0290.0170.0000.1180.0410.0000.0000.0460.0350.0520.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Probable Renter0.1400.0000.0000.0000.0000.0630.1460.0150.0020.0000.0000.0000.0060.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.1410.0091.0000.0270.0260.0680.0670.1060.0000.0430.0350.0370.0810.1580.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Renter0.1750.0000.0550.0560.0140.2370.1860.0210.0080.0000.0130.0080.0130.0020.0370.0000.0000.0130.0000.0070.0000.0120.0060.0000.0430.2620.3560.0340.0271.0000.1760.0500.0960.0030.0920.0330.0470.0580.0430.1740.0000.0000.0090.0000.0000.0000.0030.000
hhcomp_1 Adult Kids0.1440.0000.0380.0420.0000.2200.2100.0070.0190.0290.0000.0130.0380.0080.0000.0000.0000.0100.0000.0310.0000.0000.0130.0000.1220.2000.1170.0290.0260.1761.0000.1710.1700.1150.0850.1020.2650.1630.3520.2580.0000.0030.0000.0000.0000.0000.0080.000
hhcomp_2 Adults Kids0.0760.0100.0280.0310.0150.2150.2210.0320.0000.0000.0090.0040.0000.0000.0000.0170.0140.0150.0080.0000.0080.0000.0000.0000.4290.2010.2560.0170.0680.0500.1711.0000.4080.2760.2060.5020.3360.3980.8460.8760.0040.0060.0190.0000.0000.0000.0000.000
hhcomp_2 Adults No Kids0.1060.0260.0540.0530.0000.1520.1370.0060.0080.0110.0000.0000.0130.0000.0200.0000.0220.0270.0000.0000.0000.0000.0110.0000.1120.1230.2450.0000.0670.0960.1700.4081.0000.2740.2040.2630.2170.2280.4830.8720.0000.0050.0270.0000.0000.0000.0140.000
hhcomp_Single Female0.0810.0200.0160.0170.0000.0970.2240.0340.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0060.0000.2310.2130.2850.1180.1060.0030.1150.2760.2741.0000.1380.1780.1470.1540.3270.5010.0140.0090.0000.0000.0000.0000.0000.000
hhcomp_Single Male0.2110.0140.0460.0510.0240.1220.1460.0160.0010.0000.0110.0030.0020.0000.0000.0000.0320.0170.0000.0000.0000.0100.0000.0000.1920.1600.1790.0410.0000.0920.0850.2060.2040.1381.0000.1330.1090.1150.2440.3930.0000.0000.0000.0000.0000.0080.0000.000
kid_category_10.1310.0000.0000.0120.0110.1650.1330.0320.0000.0080.0140.0000.0080.0160.0000.0070.0000.0140.0000.0220.0000.0000.0000.0000.1650.0890.1000.0000.0430.0330.1020.5020.2630.1780.1331.0000.1410.1480.5460.8470.0000.0000.0070.0000.0130.0000.0000.000
kid_category_20.1470.0000.0260.0300.0000.1200.1010.0000.0100.0200.0000.0000.0180.0000.0000.0000.0360.0000.0230.0000.0000.0120.0100.0000.0680.0060.0310.0000.0350.0470.2650.3360.2170.1470.1090.1411.0000.1220.4510.8320.0000.0000.0030.0000.0000.0000.0000.000
kid_category_3+0.1750.0180.0110.0130.0200.1710.2100.0080.0000.0150.0000.0060.0120.0000.0000.0000.0000.0140.0000.0000.0210.0000.0000.0000.2740.0280.1240.0460.0370.0580.1630.3980.2280.1540.1150.1480.1221.0000.4720.9570.0200.0030.0110.0060.0000.0000.0000.006
kid_category_None/Unknown0.1000.0050.0410.0420.0000.2240.1790.0360.0080.0070.0090.0130.0110.0050.0000.0130.0080.0210.0040.0100.0040.0000.0000.0000.3460.0910.1760.0350.0810.0430.3520.8460.4830.3270.2440.5460.4510.4721.0000.9670.0030.0000.0240.0000.0000.0000.0000.000
hhsize_ordinal0.1590.0070.0390.0390.0090.1470.1630.0520.0000.0060.0140.0080.0190.0130.0120.0070.0390.0290.0330.0000.0290.0000.0260.0000.6110.2620.4330.0520.1580.1740.2580.8760.8720.5010.3930.8470.8320.9570.9671.0000.0200.0120.0210.0170.0120.0000.0000.017
campaign_8.00.0170.0000.0630.0640.0000.0150.0000.0140.0000.0110.0000.0000.0180.0270.0820.0100.0000.0560.0000.0000.0000.0000.0000.0000.0080.0000.0110.0000.0000.0000.0000.0040.0000.0140.0000.0000.0000.0200.0030.0201.0000.0000.0000.0000.0000.0000.4890.000
campaign_13.00.0000.1850.0500.0190.2050.0290.0130.0150.0040.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0060.0050.0090.0000.0000.0000.0030.0000.0120.0001.0000.0000.0000.0000.0000.5080.000
campaign_18.00.0240.0000.0380.0320.2150.0500.0000.0330.0090.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0190.0270.0000.0000.0070.0030.0110.0240.0210.0000.0001.0000.0000.0000.0000.6370.000
campaign_25.00.0060.0000.0170.0170.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0170.0000.0000.0001.0000.0000.0000.0000.500
campaign_26.00.0000.0000.0380.0460.0700.0240.0000.0040.0000.0000.0060.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0120.0000.0000.0000.0001.0000.0000.2150.000
campaign_30.00.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0470.000
description_TypeA0.0200.0940.0320.0360.1860.0330.0160.0380.0090.0000.0000.0000.0000.0040.0280.0000.0000.0350.0000.0000.0000.0060.0000.0000.0020.0000.0080.0000.0000.0030.0080.0000.0140.0000.0000.0000.0000.0000.0000.0000.4890.5080.6370.0000.2150.0471.0000.000
description_TypeB0.0060.0000.0170.0170.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0170.0000.0000.0000.5000.0000.0000.0001.000

Missing values

2023-05-23T16:57:35.789513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-23T16:57:36.848741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_ordinalincome_ordinalhhsize_ordinalcampaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
00True1.880.000000-0.000000-0.000000000000000001010000010000016.039.52.000000000
11True10.990.000000-0.000000-0.000000000010000001010000010000016.039.52.000000000
22True1.790.000000-0.000000-0.000000000000000001010000010000016.039.52.000000000
33False1.790.000000-0.000000-0.000000000000000001010000010000016.039.52.000000000
44False1.790.1005590.100559-0.000000000000000001010000010000016.039.52.000000000
55False1.790.1620110.162011-0.000000000000000001010000010000016.039.52.000000000
66False1.790.1620110.162011-0.000000000000000001010000010000016.039.52.000000000
77False1.790.1005590.100559-0.000000000000000001010000010000016.039.52.000000000
88False1.790.1005590.100559-0.000000000000000001010000010000016.039.52.000000000
99True1.790.000000-0.000000-0.000000000000000001010000010000016.039.52.000000000
Unnamed: 0first_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_ordinalincome_ordinalhhsize_ordinalcampaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
1519727396True3.190.000000-0.000000-0.000000000000000000010000010000012.062.02.000000000
1519827397True1.490.3288590.328859-0.010000000010000000010000010000012.062.02.000000000
1519927398True3.190.2163010.216301-0.000100000000000000010000010000012.062.02.000000000
1520027399True4.890.000000-0.000000-0.000000000000000000010000010000012.062.02.000000000
1520127400True2.790.1039430.103943-0.000000000000000000000000100010002.010.03.000000000
1520227401True2.290.000000-0.000000-0.000000000000000000000000100010002.010.03.000000000
1520327402True1.500.000000-0.000000-0.000000000000000000000000100010002.010.03.000000000
1520427403False3.190.3761760.376176-0.000000000000000000000000100010002.010.03.000000000
1520527404True1.490.000000-0.000000-0.000000000000000000000000100010002.010.03.000000000
1520627405True1.990.2462310.246231-0.000000000000000000000000100010002.010.03.000000000